The cybersecurity workforce is facing rapid changes, driven by advancements in artificial intelligence that are accelerating cyberthreats while also changing the nature of work for cybersecurity analysts.
But government leaders continue to highlight the cyber workforce as a key priority, including through strategic efforts like the White House’s National Cyber Strategy and the FBI’s recently released cyber strategy. Norman St. Laurent, senior product marketing manager at Cisco, said the workforce is the “foundation” of federal cybersecurity.
“Technology can provide visibility, intelligence and automation, but people still determine what risk means to the mission and what action is appropriate,” St. Laurent said during Federal News Network’s Cyber Leaders Exchange 2026. “In our security world today, I think a future-ready workforce combines strong cybersecurity fundamentals, along with the cloud, identity, some networking, lots of data automation and AI skills.”
“But just as importantly, the workforce, especially the federal workforce, must understand the agency’s mission,” he continued. “We’re not simply protecting servers here and networks. We’re actually protecting public services, investigations, intelligence operations, national defense and the citizens’ trust.”
St. Laurent argues on-the-job training and mentorship, as well as education, is key to advancing not just technical fundamentals but confidence, accountability and team trust.
“A future-ready cyber workforce . . . should have experienced professionals mentoring the junior professionals, and the junior professionals teaching what they learn, and everyone practicing together,” he said.
AI’s impact on the federal workforce
AI, however, is transforming how work gets done, disrupting entry-level roles and forcing organizations to reconsider the skills and talents they need within their IT and cybersecurity programs.
St. Laurent said advancements in generative and agentic AI models can help analyze alerts, consider potential actions and take an action to defend networks at an accelerated place.
“AI also reduces time spent on repetitive work such as alert enrichment, log searches, incident summaries, even query development, and provides fast draft reporting,” he said. “That means these junior analysts and even the more advanced analysts can concentrate on validating evidence, understanding the mission context with what they’re provided by the AI and then make decisions.”
That shift is also reflected in the tools entering the market. Cisco’s portfolio includes AI Assistant for operational support, AI Canvas for collaborative investigations, AI Defense for securing AI deployments and Cisco IQ for infrastructure insights and lifecycle intelligence. St. Laurent said these types of AI-enabled capabilities are changing the nature of cyber work, enabling analysts to spend less time on repetitive tasks and more time applying mission context, validating findings and making informed decisions.
But he said AI isn’t a replacement for humans, and that even includes junior cyber analysts. St. Laurent instead recommends thinking of AI — particularly some agentic capabilities — as a “force multiplier” that can bridge the gap between a junior analyst and an experienced cyber investigator.
“On the other hand, agencies must ensure analysts do not simply accept AI output,” he said. “Analysts should require the evidence, as an example, and examine source telemetry or even challenge the conclusions that AI is giving them — and then document their decisions.”
Managing vulnerabilities at scale
Advanced AI models have demonstrated the ability to quickly find and exploit cyber vulnerabilities, further compressing a vulnerability management cycle that has already shortened dramatically in recent years.
New federal cyber directives are attempting to rebalance how agencies prioritize vulnerabilities, partially in response to advanced AI models. (St. Laurent writes about this topic and more federal cyber issues on his blog.)
St. Laurent said AI can also be used as tool for software developers and cyber defenders to understand vulnerabilities within their own products and services.
“Agencies should use AI-assisted secure development and testing throughout the entire software lifecycle,” he said. “This includes code analysis, dependency inspection, configuration validation, adversarial testing and automated regression testing. And then findings should go should be prioritized here using a mission context.”
When patching isn’t possible, cyber leaders should focus on compensating controls like network segmentation, intrusion prevention and “virtual shielding, application controls and restricted access,” St. Laurent said.
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